基于近红外分析和化学计量学方法对不同产地灵芝快速鉴别及多糖含量测定的研究

Rapid identification of geographical origins and determination of polysaccharides contents in Ganoderma lucidum based on near infrared spectroscopy and chemometrics

该文采用近红外分析方法结合化学计量学算法对来自不同产地的灵芝进行判别分析，进一步构建了灵芝多糖含量快速预测模型。利用随机森林算法对来自4个产地的灵芝训练集预测准确率达到96．87％，独立测试集判别准确率达到93．33％；在多糖含量快速预测模型的建立过程中，采用5折交互检验优化参数，并对不同的信号处理方法进行比较，最优模型的训练集相关系数R^2cal=0．965 4，测试集相关系数R^2pre=0．851 6；校正均方根差为0．018 5，验证均方根差为0．023 6。该文利用近红外分析方法和化学计量学算法成功判别了不同产地的灵芝样品，构建的快速预测模型能够准确预测样品中多糖含量，为灵芝有效成分测定及质量评价提供新的分析方法。

Near infrared spectroscopy combined with chemometrics methods was used to distinguish Ganoderma lucidum samples collected from different origins, and a prediction model was established for rapid determine polysaccharides contents in these samples.The classification accuracy for training dataset was 96.87% , while for independent dataset was 93.33% ; as for the prediction model, 5-fold cross-validation was used to optimize the parameters, and different signal processing methods were also optimized to improve the prediction ability of the model. The best square of correlation coefficients for training dataset was 0.965 4,and 0.851 6 for validation dataset; while the root-mean-square deviation values for training dataset and validation dataset were 0.018 5 and 0.023 6, respectively. These results showed that combining near infrared spectroscopy with suitable chemometrics approaches could accuracy distinguish different origins of G. lucidum samples;the established prediction model could precious predict polysaccharides contents, the proposed method can help determine the activity compounds and quality evaluation of G. lucidum.